Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss. To tackle the challenging problem, we propose a novel deep learning-based hierarchical beam alignment method for both multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems, which learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine search manner. Specifically, a hierarchical beam alignment network (HBAN) is developed for MISO systems, which first performs coarse channel measurement using a tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The propounded HBAN is trained in two steps: the tier-1 PC and the tier-2 PC selector are first trained jointly, followed by the joint training of all the tier-2 PCs and beam predictors. Furthermore, an HBAN for MIMO systems is proposed to directly predict the optimal beam pair without performing beam alignment individually at the transmitter and receiver. Numerical results demonstrate that the proposed HBANs are superior to the state-of-art methods in both alignment accuracy and signaling overhead reduction.

翻译：快速且精确的波束对准对于利用大规模天线阵列克服严重传播损耗的毫米波通信系统中实现高质量数据传输至关重要。针对这一挑战性问题，我们提出了一种基于深度学习的新型分层波束对准方法，适用于多输入单输出（MISO）和多输入多输出（MIMO）系统。该方法学习了两级探测码本（PCs），并以由粗到细的搜索方式，利用其测量结果预测最优波束。具体而言，针对MISO系统开发了分层波束对准网络（HBAN），该网络首先使用第一级PC进行粗信道测量，然后选择第二级PC进行精细信道测量，最后基于粗测量和细测量结果共同预测最优波束。所提出的HBAN分两步训练：首先联合训练第一级PC和第二级PC选择器，随后联合训练所有第二级PC及波束预测器。此外，针对MIMO系统提出了另一种HBAN，可直接预测最优波束对，无需在发射端和接收端分别进行波束对准。数值结果表明，所提出的HBAN在对准精度和信令开销降低方面均优于现有最优方法。